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OLE 1.0 later evolved to become an architecture for software components known as the Component Object Model (COM), and later DCOM. When an OLE object is placed on the clipboard or embedded in a document, both a visual representation in native Windows formats (such as a bitmap or metafile) is stored, as well as the underlying data in its own ...
In Microsoft Windows applications programming, OLE Automation (later renamed to simply Automation [1] [2]) is an inter-process communication mechanism created by Microsoft.It is based on a subset of Component Object Model (COM) that was intended for use by scripting languages – originally Visual Basic – but now is used by several languages on Windows.
A storage is conceptually very similar to a directory on a file system. Storages can contain streams, as well as other storages. If an application wishes to persist several data objects to a file, one way to do so would be to open an IStorage that represents the contents of that file and save each of the objects within a single IStream.
Untagged unions depend on program logic to correctly identify the currently active field, which may result in strange behavior and hard-to-find bugs if that logic fails. The primary advantage of a tagged union over a simple record containing a field for each type is that it saves storage by overlapping storage for all the types. Some ...
OLE DB (Object Linking and Embedding, Database, sometimes written as OLEDB or OLE-DB) is an API designed by Microsoft that allows accessing data from a variety of sources in a uniform manner. The API provides a set of interfaces implemented using the Component Object Model (COM); it is otherwise unrelated to OLE .
In computer science, an associative array, map, symbol table, or dictionary is an abstract data type that stores a collection of (key, value) pairs, such that each possible key appears at most once in the collection. In mathematical terms, an associative array is a function with finite domain. [1] It supports 'lookup', 'remove', and 'insert ...
Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]
Here is a non-exhaustive list of typical items found in a data dictionary for columns or fields: Entity or form name or their ID (EntityID or FormID). The group this field belongs to. Field name, such as RDBMS field name; Displayed field title. May default to field name if blank. Field type (string, integer, date, etc.)